A primer of ecological statistics / (Record no. 8792)
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| fixed length control field | 11603nam a22001937a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20210407163436.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210407b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781605350646 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | e-book (NRM) |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Relator code | Gotelli, N. J. |
| Fuller form of name | Ellison, A. M. |
| 245 ## - TITLE STATEMENT | |
| Title | A primer of ecological statistics / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | USA : |
| Name of publisher, distributor, etc | Harward University, |
| Date of publication, distribution, etc | 2004. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxii, 638 p. : |
| Other physical details | ill. ; |
| Dimensions | --- |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical indexes. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Contents<br/>PART I<br/>Fundamentals of<br/>Probability and<br/>Statistical Thinking<br/>CHAPTER 1<br/>An Introduction to<br/>Probability 3<br/>What Is Probability? 4<br/>Measuring Probability 4<br/>The Probability of a Single Event:<br/>Prey Capture by Carnivorous Plants 4<br/>Estimating Probabilities by Sampling 7<br/>Problems in the Definition of Probability 9<br/>The Mathematics of Probability 11<br/>Defining the Sample Space 11<br/>Complex and Shared Events: Combining<br/>Simple Probabilities 13<br/>Probability Calculations:Milkweeds and<br/>Caterpillars 15<br/>Complex and Shared Events: Rules for<br/>Combining Sets 18<br/>Conditional Probabilities 21<br/>Bayes’ Theorem 22<br/>Summary 24<br/>CHAPTER 2<br/>Random Variables and<br/>Probability Distributions 25<br/>Discrete Random Variables 26<br/>Bernoulli Random Variables 26<br/>An Example of a Bernoulli Trial 27<br/>Many Bernoulli Trials = A Binomial Random<br/>Variable 28<br/>The Binomial Distribution 31<br/>Poisson Random Variables 34<br/>An Example of a Poisson Random Variable:<br/>Distribution of a Rare Plant 36<br/>The Expected Value of a Discrete Random<br/>Variable 39<br/>The Variance of a Discrete Random Variable 39<br/>Continuous Random Variables 41<br/>Uniform Random Variables 42<br/>The Expected Value of a Continuous Random<br/>Variable 45<br/>Normal Random Variables 46<br/>Useful Properties of the Normal<br/>Distribution 48<br/>Other Continuous Random Variables 50<br/>The Central Limit Theorem 53<br/>Summary 54<br/>CHAPTER 3<br/>Summary Statistics: Measures<br/>of Location and Spread 57<br/>Measures of Location 58<br/>The Arithmetic Mean 58<br/>Other Means 60<br/>Other Measures of Location: The Median and<br/>the Mode 64<br/>When to Use Each Measure of Location 65<br/>Measures of Spread 66<br/>The Variance and the Standard Deviation 66<br/>The Standard Error of the Mean 67<br/>Skewness, Kurtosis, and Central Moments 69<br/>Quantiles 71<br/>Using Measures of Spread 72<br/>Some Philosophical Issues Surrounding<br/>Summary Statistics 73<br/>Confidence Intervals 74<br/>Generalized Confidence Intervals 76<br/>Summary 78<br/>CHAPTER 4<br/>Framing and Testing<br/>Hypotheses 79<br/>Scientific Methods 80<br/>Deduction and Induction 81<br/>Modern-Day Induction: Bayesian Inference 84<br/>The Hypothetico-Deductive Method 87<br/>Testing Statistical Hypotheses 90<br/>Statistical Hypotheses versus Scientific<br/>Hypotheses 90<br/>Statistical Significance and P-Values 91<br/>Errors in Hypothesis Testing 100<br/>Parameter Estimation and Prediction 104<br/>Summary 105<br/>CHAPTER 5<br/>Three Frameworks for<br/>Statistical Analysis 107<br/>Sample Problem 107<br/>Monte Carlo Analysis 109<br/>Step 1: Specifying the Test Statistic 111<br/>Step 2: Creating the Null Distribution 111<br/>Step 3: Deciding on a One- or Two-Tailed<br/>Test 112<br/>Step 4: Calculating the Tail Probability 114<br/>Assumptions of the Monte Carlo Method 115<br/>Advantages and Disadvantages of the Monte<br/>Carlo Method 115<br/>Parametric Analysis 117<br/>Step 1: Specifying the Test Statistic 117<br/>Step 2: Specifying the Null Distribution 119<br/>Step 3: Calculating the Tail Probability 119<br/>Assumptions of the Parametric Method 120<br/>Advantages and Disadvantages of the<br/>Parametric Method 121<br/>Non-Parametric Analysis: A Special Case of<br/>Monte Carlo Analysis 121<br/>Bayesian Analysis 122<br/>Step 1: Specifying the Hypothesis 122<br/>Step 2: Specifying Parameters as Random<br/>Variables 125<br/>Step 3: Specifying the Prior Probability<br/>Distribution 125<br/>Step 4: Calculating the Likelihood 129<br/>Step 5: Calculating the Posterior Probability<br/>Distribution 129<br/>Step 6: Interpreting the Results 130<br/>Assumptions of Bayesian Analysis 132<br/>Advantages and Disadvantages of Bayesian<br/>Analysis 133<br/>Summary 133<br/>PART II<br/>Designing Experiments<br/>CHAPTER 6<br/>Designing Successful<br/>Field Studies 137<br/>What Is the Point of the Study? 137<br/>Are There Spatial or Temporal Differences in<br/>Variable Y? 137<br/>What Is the Effect of Factor X on<br/>Variable Y? 138<br/>Are the Measurements of Variable Y Consistent<br/>with the Predictions of Hypothesis H? 138<br/>Using the Measurements of Variable Y,<br/>What Is the Best Estimate of Parameter θ<br/>in Model Z? 139<br/>Manipulative Experiments 139<br/>Natural Experiments 141<br/>Snapshot versus Trajectory Experiments 143<br/>The Problem of Temporal Dependence 144<br/>Press versus Pulse Experiments 146<br/>Replication 148<br/>How Much Replication? 148<br/>How Many Total Replicates Are Affordable? 149<br/>The Rule of 10 150<br/>Large-Scale Studies and Environmental<br/>Impacts 150<br/>Ensuring Independence 151<br/>Avoiding Confounding Factors 153<br/>Replication and Randomization 154<br/>Designing Effective Field Experiments and<br/>Sampling Studies 158<br/>Are the Plots or Enclosures Large Enough to<br/>Ensure Realistic Results? 158<br/>What Is the Grain and Extent of the Study? 158<br/>Does the Range of Treatments or Census<br/>Categories Bracket or Span the Range of<br/>Possible Environmental Conditions? 159<br/>Have Appropriate Controls Been Established<br/>to Ensure that Results Reflect Variation Only<br/>in the Factor of Interest? 160<br/>Have All Replicates Been Manipulated in the<br/>Same Way Except for the Intended<br/>Treatment Application? 160<br/>Have Appropriate Covariates Been Measured<br/>in Each Replicate? 161<br/>Summary 161<br/>CHAPTER 7<br/>A Bestiary of Experimental<br/>and Sampling Designs 163<br/>Categorical versus Continuous Variables 164<br/>Dependent and Independent Variables 165<br/>Four Classes of Experimental Design 165<br/>Regression Designs 166<br/>ANOVA Designs 171<br/>Alternatives to ANOVA: Experimental<br/>Regression 197<br/>Tabular Designs 200<br/>Alternatives to Tabular Designs: Proportional<br/>Designs 203<br/>Summary 204<br/>CHAPTER 8<br/>Managing and Curating<br/>Data 207<br/>The First Step:Managing Raw Data 208<br/>Spreadsheets 208<br/>Metadata 209<br/>The Second Step: Storing and Curating the<br/>Data 210<br/>Storage: Temporary and Archival 210<br/>Curating the Data 211<br/>The Third Step: Checking the Data 212<br/>The Importance of Outliers 212<br/>Errors 214<br/>Missing Data 215<br/>Detecting Outliers and Errors 215<br/>Creating an Audit Trail 223<br/>The Final Step: Transforming the Data 223<br/>Data Transformations as a Cognitive Tool 224<br/>Data Transformations because the Statistics<br/>Demand It 229<br/>Reporting Results: Transformed or Not? 233<br/>The Audit Trail Redux 233<br/>Summary: The Data Management Flow<br/>Chart 235<br/>CHAPTER 9<br/>Regression 239<br/>Defining the Straight Line and Its Two<br/>Parameters 239<br/>Fitting Data to a Linear Model 241<br/>Variances and Covariances 244<br/>Least-Squares Parameter Estimates 246<br/>Variance Components and the Coefficient of<br/>Determination 248<br/>Hypothesis Tests with Regression 250<br/>The Anatomy of an ANOVA Table 251<br/>Other Tests and Confidence Intervals 253<br/>Assumptions of Regression 257<br/>Diagnostic Tests For Regression 259<br/>Plotting Residuals 259<br/>Other Diagnostic Plots 262<br/>The Influence Function 262<br/>Monte Carlo and Bayesian Analyses 264<br/>Linear Regression Using Monte Carlo<br/>Methods 264<br/>Linear Regression Using Bayesian Methods 266<br/>Other Kinds of Regression Analyses 268<br/>Robust Regression 268<br/>Quantile Regression 271<br/>Logistic Regression 273<br/>Non-Linear Regression 275<br/>Multiple Regression 275<br/>Path Analysis 279<br/>Model Selection Criteria 282<br/>Model Selection Methods for Multiple<br/>Regression 283<br/>Model Selection Methods in Path Analysis 284<br/>Bayesian Model Selection 285<br/>Summary 287<br/>CHAPTER 10<br/>The Analysis of Variance 289<br/>Symbols and Labels in ANOVA 290<br/>ANOVA and Partitioning of the Sum of<br/>Squares 290<br/>The Assumptions of ANOVA 295<br/>Hypothesis Tests with ANOVA 296<br/>Constructing F-Ratios 298<br/>A Bestiary of ANOVA Tables 300<br/>Randomized Block 300<br/>Nested ANOVA 302<br/>Two-Way ANOVA 304<br/>ANOVA for Three-Way and n-Way Designs 308<br/>Split-Plot ANOVA 308<br/>Repeated Measures ANOVA 309<br/>ANCOVA 314<br/>Random versus Fixed Factors in ANOVA 317<br/>Partitioning the Variance in ANOVA 322<br/>After ANOVA: Plotting and Understanding<br/>Interaction Terms 325<br/>Plotting Results from One-Way ANOVAs 325<br/>Plotting Results from Two-Way ANOVAs 327<br/>Understanding the Interaction Term 331<br/>Plotting Results from ANCOVAs 333<br/>Comparing Means 335<br/>A Posteriori Comparisons 337<br/>A Priori Contrasts 339<br/>Bonferroni Corrections and the Problem of<br/>Multiple Tests 345<br/>Summary 348<br/>CHAPTER 11<br/>The Analysis of Categorical<br/>Data 349<br/>Two-Way Contingency Tables 350<br/>Organizing the Data 350<br/>Are the Variables Independent? 352<br/>Testing the Hypothesis: Pearson’s Chi-square<br/>Test 354<br/>An Alternative to Pearson’s Chi-Square:<br/>The G-Test 358<br/>The Chi-square Test and the G-Test for R × C<br/>Tables 359<br/>Which Test To Choose? 363<br/>Multi-Way Contingency Tables 364<br/>Organizing the Data 364<br/>On to Multi-Way Tables! 368<br/>Bayesian Approaches to Contingency Tables 375<br/>Tests for Goodness-of-Fit 376<br/>Goodness-of-Fit Tests for Discrete<br/>Distributions 376<br/>Testing Goodness-of-Fit for Continuous<br/>Distributions: The Kolmogorov-Smirnov<br/>Test 380<br/>Summary 382<br/>CHAPTER 12<br/>The Analysis of Multivariate<br/>Data 383<br/>Approaching Multivariate Data 383<br/>The Need for Matrix Algebra 384<br/>Comparing Multivariate Means 387<br/>Comparing Multivariate Means of Two<br/>Samples: Hotelling’s T2 Test 387<br/>Comparing Multivariate Means of More Than<br/>Two Samples: A Simple MANOVA 390<br/>The Multivariate Normal Distribution 394<br/>Testing for Multivariate Normality 396<br/>Measurements of Multivariate Distance 398<br/>Measuring Distances between Two<br/>Individuals 398<br/>Measuring Distances between Two Groups 402<br/>Other Measurements of Distance 402<br/>Ordination 406<br/>Principal Component Analysis 406<br/>Factor Analysis 415<br/>Principal Coordinates Analysis 418<br/>Correspondence Analysis 421<br/>Non-Metric Multidimensional Scaling 425<br/>Advantages and Disadvantages of Ordination<br/>427<br/>Classification 429<br/>Cluster Analysis 429<br/>Choosing a Clustering Method 430<br/>Discriminant Analysis 433<br/>Advantages and Disadvantages of<br/>Classification 437<br/>Multivariate Multiple Regression 438<br/>Redundancy Analysis 438<br/>Summary 444<br/>CHAPTER 13<br/>The Measurement of Biodiversity<br/>449<br/>Estimating Species Richness 450<br/>Standardizing Diversity Comparisons through<br/>Random Subsampling<br/>Rarefaction Curves: Interpolating Species<br/>Richness 455<br/>The Expectation of the Individual-Based Rarefaction<br/>Curve 459<br/>Sample-Based Rarefaction Curves:Massachusetts<br/>Ants 461<br/>Species Richness versus Species Density 465<br/>The Statistical Comparison of Rarefaction<br/>Curves 466<br/>Assumptions of Rarefaction 467<br/>Asymptotic Estimators: Extrapolating<br/>Species Richness 470<br/>Rarefaction Curves Redux: Extrapolation and<br/>Interpolation 476<br/>Estimating Species Diversity and Evenness<br/>476<br/>Hill Numbers 479<br/>Software for Estimation of Species Diversity<br/>481<br/>Summary 482<br/>CHAPTER 14<br/>Detecting Populations<br/>and Estimating their Size 483<br/>Occupancy 485<br/>The Basic Model: One Species, One Season,<br/>Two Samples at a Range of Sites 487<br/>Occupancy of More than One Species 493<br/>A Hierarchical Model for Parameter Estimation<br/>and Modeling 495<br/>Occupancy Models for Open Populations 501<br/>Dynamic Occupancy of the Adelgid in Massachusetts<br/>505<br/>Estimating Population Size 506<br/>Mark-Recapture: The Basic Model 507<br/>Mark-Recapture Models for Open Populations<br/>516<br/>Occupancy Modeling and Mark-Recapture:<br/>Yet More Models 518<br/>Sampling for Occupancy and Abundance<br/>519<br/>Software for Estimating Occupancy and<br/>Abundance 521<br/>Summary 522<br/>APPENDIX<br/>Matrix Algebra for Ecologists 523<br/>Glossary 535<br/>Literature Cited 565<br/>Index 583 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | 1. Ecology--Statistical methods. I. Ellison, Aaron M., 1960- II. Title |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | E-Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| College of Natural Resources | College of Natural Resources | 07/04/2021 | e-book (NRM) | 07/04/2021 | 07/04/2021 | E-Book |